Update app.py

app.py

@@ -1,11 +1,91 @@
 # app.py
 
 import gradio as gr
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+import torch
+from datasets import load_dataset
+from transformers import (
+    AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments, BitsAndBytesConfig, pipeline
+)
+from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
 
-model = AutoModelForCausalLM.from_pretrained("trained_model", device_map="auto")
-tokenizer = AutoTokenizer.from_pretrained("trained_model")
+# Load dataset
+dataset = load_dataset(
+    "json",
+    data_files="https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset/resolve/main/bitext_customer_support.jsonl",
+    split="train[:100]"  # Keep it small to avoid timeouts
+)
 
+def format(example):
+    return {
+        "text": f"### Instruction:\n{example['instruction']}\n\n### Response:\n{example['output']}"
+    }
+
+dataset = dataset.map(format)
+
+# Tokenizer
+model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer.pad_token = tokenizer.eos_token
+
+def tokenize(example):
+    tokens = tokenizer(example["text"], truncation=True, padding="max_length", max_length=512)
+    tokens["labels"] = tokens["input_ids"].copy()
+    return tokens
+
+tokenized_dataset = dataset.map(tokenize, batched=True)
+
+# QLoRA setup
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    quantization_config=bnb_config
+)
+
+model.gradient_checkpointing_enable()
+model = prepare_model_for_kbit_training(model)
+
+lora_config = LoraConfig(
+    r=8,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    target_modules=["q_proj", "v_proj"],
+    task_type="CAUSAL_LM"
+)
+
+model = get_peft_model(model, lora_config)
+
+# Training
+training_args = TrainingArguments(
+    output_dir="trained_model",
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=4,
+    learning_rate=2e-4,
+    num_train_epochs=1,
+    logging_dir="./logs",
+    save_strategy="no",
+    bf16=True,
+    report_to="none",
+    optim="paged_adamw_8bit"
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset,
+    tokenizer=tokenizer
+)
+
+trainer.train()
+
+# Inference pipeline
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
 def chatbot(instruction):
@@ -17,5 +97,5 @@ gr.Interface(
     fn=chatbot,
     inputs="text",
     outputs="text",
-    title="TinyLlama QLoRA Support Bot"
+    title="Fine-Tuned TinyLlama Bitext Chatbot"
 ).launch()